Since the release of Vision Transformer(ViT)[1]}, the ViT architecture has performed quite well in the field of image recognition, so many approaches are proposed to apply this architecture to the FGVC task. For example, FFVT[2]}, AFTrans[3]}, RAMS-Trans[4]} and TransFG[5]} use ViT as the backbone network, and use the attention-map generated by the image in self-attention as the search for strong discriminative regions. Finally, the features of these regions are processed to complete the recognition task. This approach is similar to the aforementioned method based on the convolutional network. As shown in the left part of Fig.REF , the difference is that the strength of the attention map is used instead of the response of the feature map.
